{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e6335977-1dcb-a5bc-4856-184dd7bce3f9"
   },
   "source": [
    "## Trying out a linear model: \n",
    "\n",
    "Author: Alexandru Papiu ([@apapiu](https://twitter.com/apapiu), [GitHub](https://github.com/apapiu))\n",
    " \n",
    "If you use parts of this notebook in your own scripts, please give some sort of credit (for example link back to this). Thanks!\n",
    "\n",
    "\n",
    "There have been a few [great](https://www.kaggle.com/comartel/house-prices-advanced-regression-techniques/house-price-xgboost-starter/run/348739)  [scripts](https://www.kaggle.com/zoupet/house-prices-advanced-regression-techniques/xgboost-10-kfolds-with-scikit-learn/run/357561) on [xgboost](https://www.kaggle.com/tadepalli/house-prices-advanced-regression-techniques/xgboost-with-n-trees-autostop-0-12638/run/353049) already so I'd figured I'd try something simpler: a regularized linear regression model. Surprisingly it does really well with very little feature engineering. The key point is to to log_transform the numeric variables since most of them are skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "0d706811-b70c-aeab-a78b-3c7abd9978d3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import skew\n",
    "from scipy.stats.stats import pearsonr\n",
    "\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina' #set 'png' here when working on notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "603292c1-44b7-d72a-5468-e6782f311603",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"../input/train.csv\")\n",
    "test = pd.read_csv(\"../input/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "d646bb1b-56c4-9b45-d5d4-27095f61b1c0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "cb2d88d7-7f76-4b04-d28b-d2c315ae4346",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],\n",
    "                      test.loc[:,'MSSubClass':'SaleCondition']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "29fa13df-61e8-b0c2-b3a7-ea92bffd4396"
   },
   "source": [
    "###Data preprocessing: \n",
    "We're not going to do anything fancy here: \n",
    " \n",
    "- First I'll transform the skewed numeric features by taking log(feature + 1) - this will make the features more normal    \n",
    "- Create Dummy variables for the categorical features    \n",
    "- Replace the numeric missing values (NaN's) with the mean of their respective columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "9b5a3e5b-f683-3fd2-7269-4068975bbe42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f636aca7978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f63678dc240>]], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SDgMAAAAALJBwGAAAAABggYTDAAAAAAALJBwGAAAAAFigEw66AwAA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\nAACADgmHAQAAAAA69D8BSuzlTSwQm8cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f636acd00f0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 371,
       "width": 707
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)\n",
    "prices = pd.DataFrame({\"price\":train[\"SalePrice\"], \"log(price + 1)\":np.log1p(train[\"SalePrice\"])})\n",
    "prices.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "4ed54771-95c4-00e7-b2cd-569d17862878",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#log transform the target:\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "\n",
    "#log transform skewed numeric features:\n",
    "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
    "\n",
    "skewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\n",
    "skewed_feats = skewed_feats[skewed_feats > 0.75]\n",
    "skewed_feats = skewed_feats.index\n",
    "\n",
    "all_data[skewed_feats] = np.log1p(all_data[skewed_feats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "3854ab12-a4f3-4c88-fe6e-1fee08e18af2",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data = pd.get_dummies(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "5d417300-0deb-3353-cabf-95f75af62678",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#filling NA's with the mean of the column:\n",
    "all_data = all_data.fillna(all_data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "fe687685-cdac-0a89-4d71-af2d11d87a81",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#creating matrices for sklearn:\n",
    "X_train = all_data[:train.shape[0]]\n",
    "X_test = all_data[train.shape[0]:]\n",
    "y = train.SalePrice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cc4e3014-23b7-2971-ddb0-f67b03f83558"
   },
   "source": [
    "###Models\n",
    "\n",
    "Now we are going to use regularized linear regression models from the scikit learn module. I'm going to try both l_1(Lasso) and l_2(Ridge) regularization. I'll also define a function that returns the cross-validation rmse error so we can evaluate our models and pick the best tuning par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "82886739-eee6-5d7a-4be9-e1fe6ac059f1",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def rmse_cv(model):\n",
    "    rmse= np.sqrt(-cross_val_score(model, X_train, y, scoring=\"neg_mean_squared_error\", cv = 5))\n",
    "    return(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "436ce6e8-917f-8c88-3d7b-245e82a1619f",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_ridge = Ridge()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "69ff958c-dbbb-4750-3fb0-d0ac17ff6363"
   },
   "source": [
    "The main tuning parameter for the Ridge model is alpha - a regularization parameter that measures how flexible our model is. The higher the regularization the less prone our model will be to overfit. However it will also lose flexibility and might not capture all of the signal in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "f6b86166-f581-6e05-5274-d3d3516ebaf3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alphas = [0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 50, 75]\n",
    "cv_ridge = [rmse_cv(Ridge(alpha = alpha)).mean() \n",
    "            for alpha in alphas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "f8cf53ba-8441-9233-b7f5-a851d270b770"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6364300eb8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NAAAAAPRDVyu0n5vkdkmOrrW+abCzlHJKkg8neVGSY1Y6uZTy1DQB+BuS/LD9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\n1CSXJTk2SWqtp6QJwLdSSrl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b3ddL8ZRWH++pZNUWYF8e8ika8mum3IEd6Pee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\n9v9uiUgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63647baf98>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 386,
       "width": 730
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_ridge = pd.Series(cv_ridge, index = alphas)\n",
    "cv_ridge.plot(title = \"Validation - Just Do It\")\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"rmse\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "37486402-4a48-912f-84ee-a3611334b133"
   },
   "source": [
    "Note the U-ish shaped curve above. When alpha is too large the regularization is too strong and the model cannot capture all the complexities in the data. If however we let the model be too flexible (alpha small) the model begins to overfit. A value of alpha = 10 is about right based on the plot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "d42c18c9-ee70-929f-ce63-aac7f77796cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12733734668670765"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_ridge.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "863fb699-7bcd-3748-3dbb-1c9b18afee9b"
   },
   "source": [
    "So for the Ridge regression we get a rmsle of about 0.127\n",
    "\n",
    "Let' try out the Lasso model. We will do a slightly different approach here and use the built in Lasso CV to figure out the best alpha for us. For some reason the alphas in Lasso CV are really the inverse or the alphas in Ridge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "8204520c-a595-2ad2-4685-0b84cc662b84",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_lasso = LassoCV(alphas = [1, 0.1, 0.001, 0.0005]).fit(X_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "e78e6126-4de0-08ad-250b-46a3f0f48de0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12314421090977427"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_cv(model_lasso).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "abc5f43e-1c38-4c1e-cb70-a95c8d9be8de"
   },
   "source": [
    "Nice! The lasso performs even better so we'll just use this one to predict on the test set. Another neat thing about the Lasso is that it does feature selection for you - setting coefficients of features it deems unimportant to zero. Let's take a look at the coefficients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "c7be87ca-412a-cb19-1524-cd94cf698d44",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coef = pd.Series(model_lasso.coef_, index = X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "14be641e-bbe0-824d-d90f-f47698c8b5c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 110 variables and eliminated the other 178 variables\n"
     ]
    }
   ],
   "source": [
    "print(\"Lasso picked \" + str(sum(coef != 0)) + \" variables and eliminated the other \" +  str(sum(coef == 0)) + \" variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ca153134-b109-1afc-e066-44273f65d44c"
   },
   "source": [
    "Good job Lasso.  One thing to note here however is that the features selected are not necessarily the \"correct\" ones - especially since there are a lot of collinear features in this dataset. One idea to try here is run Lasso a few times on boostrapped samples and see how stable the feature selection is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "632e23a8-948f-c692-f5e9-1aa8a75f21d5"
   },
   "source": [
    "We can also take a look directly at what the most important coefficients are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "3efc02df-c877-b1fe-1807-5dd93c896c63",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imp_coef = pd.concat([coef.sort_values().head(10),\n",
    "                     coef.sort_values().tail(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "87317789-6e7e-d57f-0b54-d8ba0ee26abf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f63605ca4e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSZKkPmXwI0mSJEmS1KcMfiRJkiRJkvqUwY8kSZIkSVKfMviRJEmSJEnqUwts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iDb3zAyXZEkSZIkqfsMflRbmTmVUtxakiRJkqTZkjV+JEmSJEmSasoRP5IkSVINnbLnhBG7dmMV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6360699668>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 589,
       "width": 575
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n",
    "imp_coef.plot(kind = \"barh\")\n",
    "plt.title(\"Coefficients in the Lasso Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f6e6f820-1ec6-4a69-c309-9992b80652da"
   },
   "source": [
    "The most important positive feature is `GrLivArea` -  the above ground area by area square feet. This definitely sense. Then a few other  location and quality features contributed positively. Some of the negative features make less sense and would be worth looking into more - it seems like they might come from unbalanced categorical variables.\n",
    "\n",
    " Also note that unlike the feature importance you'd get from a random forest these are _actual_ coefficients in your model - so you can say precisely why the predicted price is what it is. The only issue here is that we log_transformed both the target and the numeric features so the actual magnitudes are a bit hard to interpret. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "cdeaa3d3-f9ad-2e06-1339-61b4425a43f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f63604cb160>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lOhD2e1d+NSsFq80IqszsoaJ0vLX7h5pdhrm2YIDW9LlGDoA7cT8EN5a3JwYM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\nd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jytpowKrYnbCsM1wHZVVe+c+XteN2dVVfcrinIf8D6XxxYEQWgpRDh0xi2b1sxLTNXo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6360507cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 371,
       "width": 390
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's look at the residuals as well:\n",
    "matplotlib.rcParams['figure.figsize'] = (6.0, 6.0)\n",
    "\n",
    "preds = pd.DataFrame({\"preds\":model_lasso.predict(X_train), \"true\":y})\n",
    "preds[\"residuals\"] = preds[\"true\"] - preds[\"preds\"]\n",
    "preds.plot(x = \"preds\", y = \"residuals\",kind = \"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4780532e-2815-e355-9a96-fb1c598f6984"
   },
   "source": [
    "The residual plot looks pretty good.To wrap it up let's predict on the test set and submit on the leaderboard:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f8da43e0-fd51-a4c9-d9b2-364d9911699a"
   },
   "source": [
    "### Adding an xgboost model:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ae9bcc1a-5106-0909-ecf7-0abc2d2ca386"
   },
   "source": [
    "Let's add an xgboost model to our linear model to see if we can improve our score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "654e4fcf-a049-921a-4783-3c6d6dcca673",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "be53a9f8-d88b-05fb-734d-3a1388d39864",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "dtrain = xgb.DMatrix(X_train, label = y)\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "\n",
    "params = {\"max_depth\":2, \"eta\":0.1}\n",
    "model = xgb.cv(params, dtrain,  num_boost_round=500, early_stopping_rounds=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "c9d5bfe5-a0a8-0b10-d54a-1dfbb6123bd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6360465d68>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hF4EcAAAA2aXNB3I65AAAAMhmbT6Qe4ykQE6HHAAAAFmm7QfypLdIIAcAAEC2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f636057eb38>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.loc[30:,[\"test-rmse-mean\", \"train-rmse-mean\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "00b8a271-0f93-c757-7e33-516c3a297628"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=2,\n",
       "       min_child_weight=1, missing=None, n_estimators=360, nthread=-1,\n",
       "       objective='reg:linear', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb = xgb.XGBRegressor(n_estimators=360, max_depth=2, learning_rate=0.1) #the params were tuned using xgb.cv\n",
    "model_xgb.fit(X_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "2b87a004-3a9a-77cc-4b5b-6540f870c028",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xgb_preds = np.expm1(model_xgb.predict(X_test))\n",
    "lasso_preds = np.expm1(model_lasso.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "1c9c640b-9e6c-a350-0691-7f6a7dc19c41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6360469f60>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UUv4o8/X9uQdjjLcC3wOeEULomdf2ffPa3gdcD1wYQjg/c/hFQAvpKVczOW1TwDVAH/DU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\nSVK9yU53mr9Fb5Jt3NDJlv5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L7bHS/lIlGo3ld9vaObKFbn/xDmeFLX8PHDlZ\n8po5yVRmXSnVvJ6IiBXNmEF5nOzsEMMw/sY0zV/nvmEYxmYyH+4nyAxX/AKZgY0fzP6TW/dSMhmQ\nW0zTzI0qvgX4o+y6H+etPYvMLJMf5k2d/yrwt8D7s0MWE9m1/cB1ZLIq92TXfgcYA95hGMYnst24\nMAzDA7yXTGeurwGYpvkLwzAezr63/5Nrj2wYhi27rziZOSsiIi2hMINQbkBgrv1w+WnvzsWjYLV0\nsrLbMteIJZKLk9jz91Jsvz6vE9JpsIEtbcPhsNMdcEE6zUI0uZhtGezr4G1XnsNQT6ae49bvmWvS\nbWvrcB/XX7Wt6FT5wp/r8ZMhrMRsY5MLbN9c/HvVvJ6IiBVNF6CYppkwDOP9wJeBBwzD+CyZo1Fn\nAu8DfMB7s4MQv5mdBH+DYRhdwH4yx6puJNPK93/lXfcRwzA+AfyxYRjfAL4ObAD+mEym5f15a8cN\nw/gzMgMg7zYM41YyQdP7gC5gp2maqezamGEY7wHuAH5iGMbngASZqfUGcJ1pmvl1Je8Bfgj82DCM\nT5IJYN4AjAA35QVJIiJNLZdByP+QXuzIUo7V40RATXUmAKdvDPDOq85ZrHnJP5pUbr/5FiIJFiIJ\nuv1urrl0M/3dPnoCbraffQqQOa9+76PHmZgO17THUnLZo6F+f9Gp8sWOWZ26wY8NKgYpQ/0dZb9v\n9fVERKxouiNeAKZp/htwCZkjUe8Dvgh8gMxxrVeapnlL3vI3An9J5ojULdl13wIuNk3zNwWXvpFM\nILIF+DxwE/Cz7NqDBXv4NPAmoAP4LPB3wDPApaZp7i9YeydwGTALfIxMYJMG/sA0zS8VrH0AeAnw\nGPDXwM3AKcDbTNP8sOUfkohIgys3P6PUgEArx4msHAUrxgZEYklm5qNsHe5d9gG72vkis6EYdz/0\ndNFrfeu+Jy1lLqpRbHhlJlhZ/vo52zdvwOks/1HA5bQvqVkpp9LriYhY0XQZlBzTNO8HXm1hXYzM\nB/2/trA2DXwm+4+VPXyVzHEvK2v/E/hPi2t/Dvy+lbUiIs2o2oL3HCvHicYmQxWPgtlsmVNZ+dLA\nielw0eyNlf1afQ+Z7Emk6mtVUmsx+mtecia37z9SNGCyZb8vIrKemjZAERGR5lVNwXvhh+5Kx4ms\nHAVz2G0kksVzGLnsTX674lqzMrn3AHB8JsLJ6QX2/mC0puyJx2XH5XQUnduykmL0y3ZkGkrecc8R\nkgUF890BN8OaXyIi60wBioiIrLtqCt5LybUfLmbnyJZl9SI5dhslg5OcwsyHlf0W43Laue0uk/mF\nOAuxBKlUelnmxqpEMs3I+adw8MnpVS9GHx7sJOBzL/t5zczHimaUYOl8GR3pEpHVpABFRETW3VrP\nzyh2FMzltBONp0hZ6DVcmL2xst9iovEUY3lT71cimUrzo1+MsaHHu6T4fjWCAyv1QLmMUjWd10RE\natGURfIiItL8VmN+Rm5+yoEjJ5fNUcmfW/LBa15AT8BjKTgBcDsdTM6Gl1yv3H6Lsduw/HpWLUQT\nHBuf544fPQGklwQnVmbJFFNNPVCx2S3BcJxj4/Ps2XeIw0enanlbIiJLKIMiIiI1W8kxn56Ah1fu\nOIOfHBgjuBCv6shS7rf4E9NhwrHkYqtcn8fBQI9vyfNz+6qmA1ckluDW75vLsgPFCvQ7snNQcjNP\nXE478USq4jGylcjPalSb0Si8Z9XUA1WTaRERqZUCFBERqdpKjvkUe25nh4vfv2iY52/urxjoFJtH\nkgsFwtHk4m/zc3UTh49O8YVvH2IhYr3IPZUGstmBwrkspQr0xyZDHDhykm/ff4xwMmn5tfJt7PGC\nzcYJCzNSpuai3Pvoce645wlLs2RK3bOXv/B0S/VA8USyps5rIiLV0hEvERGpykqO+ZR67tjkAt9/\n4Bgz85Vb+VqZR5L7bf73HjzKx/Y+wtTcyia2F85lKTbvY6jfz/0Hx4t22bKi2+/musvPxut2WFof\niSX41n1PWpolU+6e3XHPE/i85X9f2dflweWwW860iIishAIUERGpSi0DFlfjuVDdPJIT02G+9sMj\nq1YHkssOrMbe8nV4nGwaDHD9VdvoCXgsX8PtcjC/UD5gyO250s+ddLpiPVCuk1k5lTqviYhYoSNe\nIiJiWa0DFq0+9+RspOwRoWrmkURitR2zKn294nNZcmqZlbKx18cHXr998ZqHn5yyfI2Az8XkXPmB\nj5FYgieOz1b8uS9Ek1xz6Wbufujpsi2M17LzmohIjgIUEZE2Vm2R+0oGLFp57kIkwae+9gjXXXZ2\n0VqWnk4PHqeDhWT1QxNXqlJ2oNpZKd1+N9ddZiz5OVm9RqfPxRUXDXP7D49UrB1Jp7F0z/q7fWUH\nYEL5+TIrGRYpIpJPAYqISBuqtch9JQMWrX74PjEdKTocMLfnSA0T3VdDV4erbBBndVZKwOcq2anM\n6jUuv2gTl2w/lbsferpiRmPzad1V3bNyAzBLdTJbjWGRIiI5jt27d9d7D7KOFhZiu+u9B6nM7/cA\nsLCgYtNWV497nSuYPjEdJpZIkU5DLJFiNhTj4K+nGD4lwECPr+hzOzvcPHB4vGyR+in9HVx58Zk1\nPTcnGk/y9MQ8LzvvtGV7XrvmveXFEknuffQ4Xo+DTYOdRdcM9vk4+OspovHlx8u6/G7efNnZXHXx\nMFdefGbJn/Fgn4+fHhovO3E+uBDnZeedVvb1chmaM4e6a75nxQz0+HjZeafxgrM2cN5ZA/zeC08v\n+37ajf7+bh+615X5/Z6/quV5KpIXEWkzKy1UX8mAxWqGHeYXpVvp3LXWovEUJ6Yj3PLtx/jzm+8v\n2q0sl2HY2OPFbnv2cbsN/D4nm0/rrniUrifgweMq/5/n3M8m93qbBgMEfC6cDhsBn2ux6D6X0ViN\noZiFinUyExFZDcqgtBllUJqDfivTPtb7Xo9Nhrj7Z08TS6RKrkmm0rzgrA10dhT/QDvQ42P4lABP\nT8yTTKVJpdN0eF2c0t/BdZcZZY/55J47+swsoQpzSRLJFOc/byPmU9P8+BfH65Y5KSYUSZTMNp2c\nDfPg4RNLivTTwPxCnP82T7BpsHSGCuDpE/Pc+8vflM2gpNJpzjtrgIEen6WMxkrumVRHf3+3D93r\nymrNoKgGRUSkjaykyD1fuYGFlWwd7uNNLz+Lz935S6Lx0oFSKg2PHjnJTw6MsUqdgldVqcnp5bI9\n08FoxWnrtdb5lKsdgZXdMxGR9aQARUSkjaykyL2YSh+KC+UX55cLTnJ+8PAzxMtke+qtsKXyStow\n51gplF9JO99q75mIyHpTDYqISBvJffgtZ61mWRROM7ci0cDBCSyfnF5NhqqctagZERFpFgpQRETa\nTL0+/NZS6L7Sk11et2NJsfpqczntS7JNqzVt3Wrxu4hIK9IRLxGRNlOPWRZWjj4VY6P2IKUn4OZP\n3ngeN+87yLHx+RqvUl4imV6SbVrN41mqGRGRdqUARUSkDa33h18rR5+KcTrtNdeguF0Ohvr9Zaef\nr5TDbltWT1Lu9Xo7PVVnqFQzIiLtRke8RETa2HrNsrBy9KlQt9/Na15yJh0eR02vuRBJFJ0VsppH\nvhLJ1LJ6klLHszaf1s2Nb75Ax7NERCpQBkVERNaclaNP+Tb2ernusrMBsNlqiyjy2yXnZ4wOHDnJ\nf9z75JI5JbUqVU9SLEO1/exTAJiYCK74dUVEWpkyKCIisi52jmwh4HNVXNfhdfKB1z+frcN97N0/\nWnGgYymlZoXcf3B8VYITqFxPomnrIiLVU4AiIiLrYutwH+9+9Tm4nOX/07Oh28tQv7/mwvqcYsFD\nLdf0e4sfNlC7XxGRtaEARURE1tTYZIjDT04t1oPccPV2OktkUvI/9NdaWF94nXxHnpklFLV2zAwg\n4HOxc2SL2v2KiKwj1aCIiMgSY5MhZoJRejpXNrAxf2r8QixBh9tJb7aV8btefU7FNsfWpt476O30\nEFyIl22XnNvLydkIqSqagvV1ebhk+6lcsv1Uyx3PxiZDHHlmFoDNp3XreJeISJUUoIiICFA+oKg2\nU5CbGp/fajcYjhMMx9mz7xDXX7WtYptjK4X1G3t9Fa9TbC9WFGZhKrX7PXx0ilu/Z3JyJkwqO7zF\nbsscWbvu8rMZGOhcXLtaQaCISCtSgCIi0sRWM9tRKaCoJkgpNzV+NhRj7/5Rdu/aUfRDf/57KjdT\nJD+AKBc8VDvB3udxMNDjqyowO3x0in/8xi+XFfSn0nBiJsJnv/4oPd0dANz8jQOrEgSKiLQqBSgi\nIk1oNbMdYD2gsMJKIfrUXHTZgMNi78nndWIjjd3G0qxEj4/rLjOWvdfCgK3aovjc9Plqg71K3cYW\nokk++7VHCMcSS/azkiBQRKRVKUAREWkyq53tsBpQHDhykrnsa5arrbBS3L4QjXPk+NziNcq9p0Kp\nNEQL2gSXCtgu3DZYVaF9Ipm2vDZnbDLEydmIpXXpEpevNggUEWllClBERJrMamY7wFpAMR+O86nb\nD5D7fF0ui2GluD2Vgn/7wePc/fOn2DmypepjWPnvs1xwMzUXxe20E05am3uSP9zRqplglGi08vVL\nBSc5xbJKIiLtSG2GRUSaSDXHp6zKBRSV5H++TqXhxHSYT9/xKPceOL5kXa64vZKFSIJj4/P8w9cO\n8NT4vOX95uTeZ7ngZj4cryorUmoyfDk9nR48HkdVzykmFxyJiLQ7BSgiIk3ESraj2g+6VgOK4q+V\n5F+++xi7b3mQw0enFh/fObKFbr+1D/rReIrqD1Zl3ucTx2eZmAmXXWcjXXLuSqFKk+GLGer3s6Hb\nW3Gd3W4r+/1agiMRkVakAEVEpIlYyXbU8kG3moCiUCoNx8b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J3LzYNM2x7GNfMgzjTuCP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63601ecc88>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 371,
       "width": 404
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({\"xgb\":xgb_preds, \"lasso\":lasso_preds})\n",
    "predictions.plot(x = \"xgb\", y = \"lasso\", kind = \"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "74c9bdd2-afbb-5fdf-a776-a8eebfa30d12"
   },
   "source": [
    "Many times it makes sense to take a weighted average of uncorrelated results - this usually imporoves the score although in this case it doesn't help that much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "623ed0fe-0150-5226-db27-90a321061d52",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds = 0.7*lasso_preds + 0.3*xgb_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "569d7154-e3b5-84ab-1d28-57bdc02955d9",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"id\":test.Id, \"SalePrice\":preds})\n",
    "solution.to_csv(\"ridge_sol.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fe4ec3c9-ae45-e01e-d881-32da250d44ba"
   },
   "source": [
    "### Trying out keras?\n",
    "\n",
    "Feedforward Neural Nets doesn't seem to work well at all...I wonder why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "12121592-5b16-5957-6c54-3fe84bc6708a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.regularizers import l1\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "246a88ac-3963-a603-cf33-eb2976737c98",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = StandardScaler().fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "04936965-5441-3989-1f07-97138b331dbc",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_tr, X_val, y_tr, y_val = train_test_split(X_train, y, random_state = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "5223b976-c02e-062e-5c73-60516bf70fa5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1095, 288)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "7b7e0df1-ea9c-5dcb-41cd-f79509218a20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00573733,  0.68066137, -0.46001991, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503],\n",
       "       [-1.12520184,  0.60296111,  0.03113183, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503],\n",
       "       [-1.12520184, -0.02865265, -0.74027492, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503],\n",
       "       ..., \n",
       "       [ 0.16426234, -0.87075036, -0.81954431, ..., -0.11785113,\n",
       "        -2.13834494, -0.30599503],\n",
       "       [ 0.92361154, -0.30038284, -0.44275864, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503],\n",
       "       [ 0.83656519,  1.98505948,  0.46455838, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "14ef62de-56e3-03cc-00c6-a5e2307d1b6a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, input_dim=288, kernel_regularizer=<keras.reg...)`\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "#model.add(Dense(256, activation=\"relu\", input_dim = X_train.shape[1]))\n",
    "model.add(Dense(1, input_dim = X_train.shape[1], W_regularizer=l1(0.001)))\n",
    "\n",
    "model.compile(loss = \"mse\", optimizer = \"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "082332bc-b36b-30db-1e0e-c212fba98b58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1)                 289       \n",
      "=================================================================\n",
      "Total params: 289\n",
      "Trainable params: 289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "ad155a35-1d0b-c42f-9bdf-77ff389ddfd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1095 samples, validate on 365 samples\n",
      "Epoch 1/10\n",
      "1095/1095 [==============================] - 0s - loss: 147.0313 - val_loss: 149.9484\n",
      "Epoch 2/10\n",
      "1095/1095 [==============================] - 0s - loss: 144.6278 - val_loss: 150.5536\n",
      "Epoch 3/10\n",
      "1095/1095 [==============================] - 0s - loss: 142.8769 - val_loss: 151.4110\n",
      "Epoch 4/10\n",
      "1095/1095 [==============================] - 0s - loss: 141.3150 - val_loss: 152.4455\n",
      "Epoch 5/10\n",
      "1095/1095 [==============================] - 0s - loss: 139.8918 - val_loss: 153.7267\n",
      "Epoch 6/10\n",
      "1095/1095 [==============================] - 0s - loss: 138.4870 - val_loss: 155.0268\n",
      "Epoch 7/10\n",
      "1095/1095 [==============================] - 0s - loss: 137.1905 - val_loss: 156.3740\n",
      "Epoch 8/10\n",
      "1095/1095 [==============================] - 0s - loss: 135.9163 - val_loss: 157.8431\n",
      "Epoch 9/10\n",
      "1095/1095 [==============================] - 0s - loss: 134.6428 - val_loss: 159.4450\n",
      "Epoch 10/10\n",
      "1095/1095 [==============================] - 0s - loss: 133.3451 - val_loss: 161.2576\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(X_tr, y_tr, validation_data = (X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "d6c6354f-047b-1d8e-c024-15bb5d570f15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f630bd72a58>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GOGnN4+ckOXuTl3jeGOP0VeMPTXJ6kkclOSbJDUnemeScMcZ7tmveAADQzbZEelVVkj9M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\n+f9YD+jib02vbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f630bdafeb8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 372
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(model.predict(X_val)[:,0]).hist()"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 6,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
